Search results for: Automatic Geometric Theorem Proving

Authors: Ion Smeureanu, Marian Dardala, Adriana Reveiu

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The new programming technologies allow for the creation of components which can be automatically or manually assembled to reach a new experience in knowledge understanding and mastering or in getting skills for a specific knowledge area. The project proposes an interactive framework that permits the creation, combination and utilization of components that are specific to mathematical training in high schools. The main framework-s objectives are: • authoring lessons by the teacher or the students; all they need are simple operating skills for Equation Editor (or something similar, or Latex); the rest are just drag & drop operations, inserting data into a grid, or navigating through menus • allowing sonorous presentations of mathematical texts and solving hints (easier understood by the students) • offering graphical representations of a mathematical function edited in Equation • storing of learning objects in a database • storing of predefined lessons (efficient for expressions and commands, the rest being calculations; allows a high compression) • viewing and/or modifying predefined lessons, according to the curricula The whole thing is focused on a mathematical expressions minicompiler, storing the code that will be later used for different purposes (tables, graphics, and optimisations). Programming technologies used. A Visual C# .NET implementation is proposed. New and innovative digital learning objects for mathematics will be developed; they are capable to interpret, contextualize and react depending on the architecture where they are assembled.

Keywords: Adaptor, automatic assembly learning component and user control.

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Authors: Kai Häussermann, Christoph Hubig, Paul Levi, Frank Leymann, Oliver Siemoneit, Matthias Wieland, Oliver Zweigle

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Using spatial models as a shared common basis of information about the environment for different kinds of contextaware systems has been a heavily researched topic in the last years. Thereby the research focused on how to create, to update, and to merge spatial models so as to enable highly dynamic, consistent and coherent spatial models at large scale. In this paper however, we want to concentrate on how context-aware applications could use this information so as to adapt their behavior according to the situation they are in. The main idea is to provide the spatial model infrastructure with a situation recognition component based on generic situation templates. A situation template is – as part of a much larger situation template library – an abstract, machinereadable description of a certain basic situation type, which could be used by different applications to evaluate their situation. In this paper, different theoretical and practical issues – technical, ethical and philosophical ones – are discussed important for understanding and developing situation dependent systems based on situation templates. A basic system design is presented which allows for the reasoning with uncertain data using an improved version of a learning algorithm for the automatic adaption of situation templates. Finally, for supporting the development of adaptive applications, we present a new situation-aware adaptation concept based on workflows.

Keywords: context-awareness, ethics, facilitation of system use through workflows, situation recognition and learning based on situation templates and situation ontology's, theory of situationaware systems

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Authors: R. Prakash, B. Meenakshipriya, R. Kumaravelan

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Fresh water is one of the resources which is getting depleted day by day. A wise method to address this issue is by the application of renewable energy-sun irradiation and by means of decentralized, cheap, energetically self-sufficient, robust and simple to operate plants, distillates can be obtained from sea, river or even sewage. Solar desalination is a technique used to desalinate water using solar energy. The present work deals with the comprehensive design and simulation of solar tracking system using LabVIEW, temperature and mass flow rate control of the solar desalination plant using LabVIEW and also analysis of single phase inverter circuit with LC filters for solar pumping system in MATLAB. The main objective of this work is to improve the performance of solar desalination system using automatic tracking system, output control using temperature and mass flow rate control system and also to reduce the harmonic distortion in the solar pumping system by means of LC filters. The simulation of single phase inverter was carried out using MATLAB and the output waveforms were analyzed. Simulations were performed for optimum output temperature control, which in turn controls the mass flow rate of water in the thermal collectors. Solar tracking system was accomplished using LABVIEW and was tested successfully. The thermal collectors are tracked in accordance with the sun’s irradiance levels, thereby increasing the efficiency of the thermal collectors.

Keywords: Desalination, Electro dialysis, LabVIEW, MATLAB, PWM inverter, Reverse osmosis.

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Authors: Carmen Grigoraş, Victor Grigoraş, Daniela Boişteanu

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The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient-s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation.

Keywords: Artificial neural networks, feature extraction, obstructive sleep apnea syndrome, pattern recognition, signalprocessing.

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Authors: N. Yuvaraj, V. B. Prakash, D. Venkatraj

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This paper may be considered as combination of both pervasive computing and Differential GPS (global positioning satellite) which relates to control automatic traffic signals in such a way as to pre-empt normal signal operation and permit lifesaving vehicles. Before knowing the arrival of the lifesaving vehicles from the signal there is a chance of clearing the traffic. Traffic signal preemption system includes a vehicle equipped with onboard computer system capable of capturing diagnostic information and estimated location of the lifesaving vehicle using the information provided by GPS receiver connected to the onboard computer system and transmitting the information-s using a wireless transmitter via a wireless network. The fleet management system connected to a wireless receiver is capable of receiving the information transmitted by the lifesaving vehicle .A computer is also located at the intersection uses corrected vehicle position, speed & direction measurements, in conjunction with previously recorded data defining approach routes to the intersection, to determine the optimum time to switch a traffic light controller to preemption mode so that lifesaving vehicles can pass safely. In case when the ambulance need to take a “U" turn in a heavy traffic area we suggest a solution. Now we are going to make use of computerized median which uses LINKED BLOCKS (removable) to solve the above problem.

Keywords: Ubiquitous computing, differential GPS, fleet management system, wireless transmitter and receiver computerized median i.e. linked blocks (removable).

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Authors: Kashyap L. Mokariya, Varsha A. Shah, Makarand M. Lokhande

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As the current status and growth of Indian automobile industry is remarkable, transportation sectors are the main concern in terms of energy security and climate change. Due to rising demand of fuel and its dependency on foreign countries that affects the GDP of nation, suggests that penetration of electrical vehicle will increase in near future. So in this context analysis is done if the 10 percent of conventional vehicles including cars, three wheelers and two wheelers becomes electrical vehicles in near future which is also a part of Nations Electric Mobility Mission Plan then the saving which improves the nation’s economy is analyzed in detail. Whether the Indian electricity grid is capable of taking this load with current generation and demand all over the country is also analyzed in detail. Current situation of Indian grid is analyzed and how the gap between generation and demand can be reduced is discussed in terms of increasing generation capacity and energy conservation measures. Electrical energy conservation measures in Industry and especially in rural areas have been analyzed to improve performance of Indian electricity grid in context of electrical vehicle penetration in near future. Author was a part of Vishvakarma yojna in which energy losses were measured in 255 villages of Gujarat and solutions were suggested to mitigate them and corresponding reports was submitted to the authorities of Gujarat government.

Keywords: Vehicle penetration, feasibility, Energy conservation, future grid, Energy security, Automatic pf controller.

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Authors: Majid Yazdandoust

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This study, tries to suggest a design method based on displacement using finite difference numerical modeling in reinforcing soil retaining wall with steel strip. In this case, dynamic loading characteristics such as duration, frequency, peak ground acceleration, geometrical characteristics of reinforced soil structure and type of the site are considered to correct the pseudo static method and finally introduce the pseudo static coefficient as a function of seismic performance level and peak ground acceleration. For this purpose, the influence of dynamic loading characteristics, reinforcement length, height of reinforced system and type of the site are investigated on seismic behavior of reinforcing soil retaining wall with steel strip. Numerical results illustrate that the seismic response of this type of wall is highly dependent to cumulative absolute velocity, maximum acceleration, and height and reinforcement length so that the reinforcement length can be introduced as the main factor in shape of failure. Considering the loading parameters, geometric parameters of the wall and type of the site showed that the used method in this study leads to efficient designs in comparison with other methods, which are usually based on limit-equilibrium concept. The outputs show the over-estimation of equilibrium design methods in comparison with proposed displacement based methods here.

Keywords: Pseudo static coefficient, seismic performance design, numerical modeling, steel strip reinforcement, retaining walls, cumulative absolute velocity, failure shape.

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Authors: Sungju Yun, Anjin Park, Keechul Jung

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Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where on-line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of several repeated frames within temporal distances, we consider all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.

Keywords: Capture Devices, High-Level Motion, Motion Segmentation, Normalized Cuts

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Authors: Johan Wall, Johan Fredin, Anders Jönsson, Göran Broman

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Designing modern machine tools is a complex task. A simulation tool to aid the design work, a virtual machine, has therefore been developed in earlier work. The virtual machine considers the interaction between the mechanics of the machine (including structural flexibility) and the control system. This paper exemplifies the usefulness of the virtual machine as a tool for product development. An optimisation study is conducted aiming at improving the existing design of a machine tool regarding weight and manufacturing accuracy at maintained manufacturing speed. The problem can be categorised as constrained multidisciplinary multiobjective multivariable optimisation. Parameters of the control and geometric quantities of the machine are used as design variables. This results in a mix of continuous and discrete variables and an optimisation approach using a genetic algorithm is therefore deployed. The accuracy objective is evaluated according to international standards. The complete systems model shows nondeterministic behaviour. A strategy to handle this based on statistical analysis is suggested. The weight of the main moving parts is reduced by more than 30 per cent and the manufacturing accuracy is improvement by more than 60 per cent compared to the original design, with no reduction in manufacturing speed. It is also shown that interaction effects exist between the mechanics and the control, i.e. this improvement would most likely not been possible with a conventional sequential design approach within the same time, cost and general resource frame. This indicates the potential of the virtual machine concept for contributing to improved efficiency of both complex products and the development process for such products. Companies incorporating such advanced simulation tools in their product development could thus improve its own competitiveness as well as contribute to improved resource efficiency of society at large.

Keywords: Machine tools, Mechatronics, Non-deterministic, Optimisation, Product development, Virtual machine

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Authors: L. Yu, W. K. Li, S. K. Ong, A. Y. C. Nee

Abstract:

In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.

Keywords: Augmented reality framework, server-client model, vision-based tracking, image search.

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Authors: Jing Jia, Shanqing Huang, Fang Liu, Qiang Ren, Gui Li, Mengyun Cheng, Chufeng Jin, Yican Wu

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Medical image registration is the key technology in image guided radiation therapy (IGRT) systems. On the basis of the previous work on our IGRT prototype with a biorthogonal x-ray imaging system, we described a method focused on the 2D/2D rigid-body registration using multiresolution pyramid based mutual information in this paper. Three key steps were involved in the method ： firstly, four 2D images were obtained including two x-ray projection images and two digital reconstructed radiographies（DRRs ） as the input for the registration ； Secondly, each pair of the corresponding x-ray image and DRR image were matched using multiresolution pyramid based mutual information under the ITK registration framework ； Thirdly, we got the final couch offset through a coordinate transformation by calculating the translations acquired from the two pairs of the images. A simulation example of a parotid gland tumor case and a clinical example of an anthropomorphic head phantom were employed in the verification tests. In addition, the influence of different CT slice thickness were tested. The simulation results showed that the positioning errors were 0.068±0.070, 0.072±0.098, 0.154±0.176mm along three axes which were lateral, longitudinal and vertical. The clinical test indicated that the positioning errors of the planned isocenter were 0.066, 0.07, 2.06mm on average with a CT slice thickness of 2.5mm. It can be concluded that our method with its verified accuracy and robustness can be effectively used in IGRT systems for patient setup.

Keywords: 2D/2D registration, image guided radiation therapy, multi resolution pyramid, mutual information.

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: Data mining, digital libraries, digital preservation, file format.

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Authors: Dinesh Acharya U., N. V. Subba Reddy, Krishnamoorthi Makkithaya

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The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average boundary length from the minimal bounding box are used in the recognition of numeral. The effect of each feature and their combination in the numeral classification is analyzed using nearest neighbor classifiers. It is common to combine multiple categories of features into a single feature vector for the classification. Instead, separate classifiers can be used to classify based on each visual feature individually and the final classification can be obtained based on the combination of separate base classification results. One popular approach is to combine the classifier results into a feature vector and leaving the decision to next level classifier. This method is extended to extract a better information, possibility distribution, from the base classifiers in resolving the conflicts among the classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy k-NN) as base classifier for individual feature sets, the results of which together forms the feature vector for the final k Nearest Neighbor (k-NN) classifier. Testing is done, using different features, individually and in combination, on a database containing 1600 samples of different numerals and the results are compared with the results of different existing methods.

Keywords: Fuzzy k Nearest Neighbor, Multiple Classifiers, Numeral Recognition, Structural features.

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: Algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction.

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Authors: Hazem M. El-Bakry

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In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.

Keywords: Hopfield Neural Networks, Cross Correlation, Nuclear Magnetic Resonance, Magnetic Resonance Spectroscopy, Fast Fourier Transform.

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Authors: Kifah Tout, Nisrine Sinno, Mohamad Mikati

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Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called in neurology, a seizure. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don-t know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. Our approach is to use the neural network tool to detect interictal states and to predict from those states the upcoming seizure ( ictal state). Analysis of the EEG signal based on neural networks is used for the classification of EEG as either seizure or non-seizure. By applying prediction methods it will be possible to predict the upcoming seizure from non-seizure EEG. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. Preictal, ictal, and post ictal EEG recordings are available on such patients for analysis The system will be induced by taking a body of samples then validate it using another. Distinct from the two first ones a third body of samples is taken to test the network for the achievement of optimum prediction. Several methods will be tried 'Backpropagation ANN' and 'RBF'.

Keywords: Artificial neural network (ANN), automatic prediction, epileptic seizures analysis, genetic algorithm.

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Authors: K. S. Sivakumaran, T. Thanga, B. Halabieh

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The end panels of a large rectangular industrial duct, which experience significant internal pressures, also experience considerable transverse shear due to transfer of gravity loads to the supports. The current design practice of such thin plate panels for shear load is based on methods used for the design of plate girder webs. The structural arrangements, the loadings and the resulting behavior associated with the industrial duct end panels are, however, significantly different from those of the web of a plate girder. The large aspect ratio of the end panels gives rise to multiple bands of tension fields, whereas the plate girder web design is based on one tension field. In addition to shear, the industrial end panels are subjected to internal pressure which in turn produces significant membrane action. This paper reports a study which was undertaken to review the current industrial analysis and design methods and to propose a comprehensive method of designing industrial duct end panels for shear resistance. In this investigation, a nonlinear finite element model was developed to simulate the behavior of industrial duct end panel, along with the associated edge stiffeners, subjected to transverse shear and internal pressures. The model considered the geometric imperfections and constitutive relations for steels. Six scale independent dimensionless parameters that govern the behavior of such end panel were identified and were then used in a parametric study. It was concluded that the plate slenderness dominates the shear strength of stockier end panels, and whereas, both the plate slenderness and the aspect ratio influence the shear strength of slender end panels. Based on these studies, this paper proposes design aids for estimating the shear strength of rectangular duct end panels.

Keywords: Thin plate, transverse shear, tension field, finite element analysis, parametric study, design.

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Authors: Bremananth R., Veerabadran C. S., Andy W. H. Khong

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Automatic reading of handwritten cheque is a computationally complex process and it plays an important role in financial risk management. Machine vision and learning provide a viable solution to this problem. Research effort has mostly been focused on recognizing diverse pitches of cheques and demand drafts with an identical outline. However most of these methods employ templatematching to localize the pitches and such schemes could potentially fail when applied to different types of outline maintained by the bank. In this paper, the so-called outline problem is resolved by a cheque information tree (CIT), which generalizes the localizing method to extract active-region-of-entities. In addition, the weight based density plot (WBDP) is performed to isolate text entities and read complete pitches. Recognition is based on texture features using neural classifiers. Legal amount is subsequently recognized by both texture and perceptual features. A post-processing phase is invoked to detect the incorrect readings by Type-2 grammar using the Turing machine. The performance of the proposed system was evaluated using cheque and demand drafts of 22 different banks. The test data consists of a collection of 1540 leafs obtained from 10 different account holders from each bank. Results show that this approach can easily be deployed without significant design amendments.

Keywords: Cheque reading, Connectivity checking, Text localization, Texture analysis, Turing machine, Signature verification.

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Authors: U. Gudaru, D. R. Patil

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Electrical distribution systems are incurring large losses as the loads are wide spread, inadequate reactive power compensation facilities and their improper control. A comprehensive static VAR compensator consisting of capacitor bank in five binary sequential steps in conjunction with a thyristor controlled reactor of smallest step size is employed in the investigative work. The work deals with the performance evaluation through analytical studies and practical implementation on an existing system. A fast acting error adaptive controller is developed suitable both for contactor and thyristor switched capacitors. The switching operations achieved are transient free, practically no need to provide inrush current limiting reactors, TCR size minimum providing small percentages of nontriplen harmonics, facilitates stepless variation of reactive power depending on load requirement so as maintain power factor near unity always. It is elegant, closed loop microcontroller system having the features of self regulation in adaptive mode for automatic adjustment. It is successfully tested on a distribution transformer of three phase 50 Hz, Dy11, 11KV/440V, 125 KVA capacity and the functional feasibility and technical soundness are established. The controller developed is new, adaptable to both LT & HT systems and practically established to be giving reliable performance.

Keywords: Binary Sequential switched capacitor bank, TCR, Nontriplen harmonics, step less Q control, transient free

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: Communication signal, feature extraction, holder coefficient, improved cloud model.

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to gather information in unknown environments to achieve simultaneous localization and mapping of the environment. This technology has a wide range of applications in autonomous driving, virtual reality, and other related fields. Currently, the research advancements related to VSLAM can maintain high accuracy in static environments. But in dynamic environments, the presence of moving objects in the scene can reduce the stability of the VSLAM system, leading to inaccurate localization and mapping, or even system failure. In this paper, a robust VSLAM method was proposed to effectively address the challenges in dynamic environments. We proposed a dynamic region removal scheme based on a semantic segmentation neural network and geometric constraints. Firstly, a semantic segmentation neural network is used to extract the prior active motion region, prior static region, and prior passive motion region in the environment. Then, the lightweight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static regions and dynamic regions. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under a high dynamic environment.

Keywords: Dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM.

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Authors: Sarah Motiee, Azadeh Nematzadeh, Mehrnoush Shamsfard

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Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques to extract phrases from documents and the query and doing stemming on words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done flexible and in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.

Keywords: Document ranking, Ontology, Spread activation algorithm, Annotation.

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Authors: Mehrnoush Shamsfard, Azadeh Nematzadeh, Sarah Motiee

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Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques for extracting phrases and stemming words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.

Keywords: Document ranking, Ontology, Spread activation algorithm, Annotation.

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

Abstract:

Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: Airborne laser scanning, digital terrain models, filtering, forested areas.

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

Abstract:

Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: Kohtaroh Miyamoto

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As enterprise computing becomes more and more complex, the costs and technical challenges of IT system maintenance and support are increasing rapidly. One popular approach to managing IT system maintenance is to prepare and use a FAQ (Frequently Asked Questions) system to manage and reuse systems knowledge. Such a FAQ system can help reduce the resolution time for each service incident ticket. However, there is a major problem where over time the knowledge in such FAQs tends to become outdated. Much of the knowledge captured in the FAQ requires periodic updates in response to new insights or new trends in the problems addressed in order to maintain its usefulness for problem resolution. These updates require a systematic approach to define the exact portion of the FAQ and its content. Therefore, we are working on a novel method to hierarchically structure the FAQ and automate the updates of its structure and content. We use structured information and the unstructured text information with the timelines of the information in the service incident tickets. We cluster the tickets by structured category information, by keywords, and by keyword modifiers for the unstructured text information. We also calculate an urgency score based on trends, resolution times, and priorities. We carefully studied the tickets of one of our projects over a 2.5-year time period. After the first 6 months we started to create FAQs and confirmed they improved the resolution times. We continued observing over the next 2 years to assess the ongoing effectiveness of our method for the automatic FAQ updates. We improved the ratio of tickets covered by the FAQ from 32.3% to 68.9% during this time. Also, the average time reduction of ticket resolution was between 31.6% and 43.9%. Subjective analysis showed more than 75% reported that the FAQ system was useful in reducing ticket resolution times.

Keywords: FAQ System, Resolution Time, Service Incident Tickets, IT System Maintenance.

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Authors: Ahmed Badawi, J. Michael Johnson, Mohamed Mahfouz

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This paper proposes new enhancement models to the methods of nonlinear anisotropic diffusion to greatly reduce speckle and preserve image features in medical ultrasound images. By incorporating local physical characteristics of the image, in this case scatterer density, in addition to the gradient, into existing tensorbased image diffusion methods, we were able to greatly improve the performance of the existing filtering methods, namely edge enhancing (EE) and coherence enhancing (CE) diffusion. The new enhancement methods were tested using various ultrasound images, including phantom and some clinical images, to determine the amount of speckle reduction, edge, and coherence enhancements. Scatterer density weighted nonlinear anisotropic diffusion (SDWNAD) for ultrasound images consistently outperformed its traditional tensor-based counterparts that use gradient only to weight the diffusivity function. SDWNAD is shown to greatly reduce speckle noise while preserving image features as edges, orientation coherence, and scatterer density. SDWNAD superior performances over nonlinear coherent diffusion (NCD), speckle reducing anisotropic diffusion (SRAD), adaptive weighted median filter (AWMF), wavelet shrinkage (WS), and wavelet shrinkage with contrast enhancement (WSCE), make these methods ideal preprocessing steps for automatic segmentation in ultrasound imaging.

Keywords: Nonlinear anisotropic diffusion, ultrasound imaging, speckle reduction, scatterer density estimation, edge based enhancement, coherence enhancement.

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Authors: V Krishnaveni, S Jayaraman, A Gunasekaran, K Ramadoss

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The ElectroEncephaloGram (EEG) is useful for clinical diagnosis and biomedical research. EEG signals often contain strong ElectroOculoGram (EOG) artifacts produced by eye movements and eye blinks especially in EEG recorded from frontal channels. These artifacts obscure the underlying brain activity, making its visual or automated inspection difficult. The goal of ocular artifact removal is to remove ocular artifacts from the recorded EEG, leaving the underlying background signals due to brain activity. In recent times, Independent Component Analysis (ICA) algorithms have demonstrated superior potential in obtaining the least dependent source components. In this paper, the independent components are obtained by using the JADE algorithm (best separating algorithm) and are classified into either artifact component or neural component. Neural Network is used for the classification of the obtained independent components. Neural Network requires input features that exactly represent the true character of the input signals so that the neural network could classify the signals based on those key characters that differentiate between various signals. In this work, Auto Regressive (AR) coefficients are used as the input features for classification. Two neural network approaches are used to learn classification rules from EEG data. First, a Polynomial Neural Network (PNN) trained by GMDH (Group Method of Data Handling) algorithm is used and secondly, feed-forward neural network classifier trained by a standard back-propagation algorithm is used for classification and the results show that JADE-FNN performs better than JADEPNN.

Keywords: Auto Regressive (AR) Coefficients, Feed Forward Neural Network (FNN), Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm, Polynomial Neural Network (PNN).

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Authors: H. Shayeghi, A. Jalili

Abstract:

This paper presents a new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GA-s) for solution of the Automatic generation Control (AGC) problem in a deregulated electricity environment. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions, because it is a computationally expensive combinatorial optimization problem. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, the membership functions are tuned automatically using a modified GA-s based on the hill climbing method. The motivation for using the modified GA-s is to reduce fuzzy system effort and take large parametric uncertainties into account. The global optimum value is guaranteed using the proposed method and the speed of the algorithm-s convergence is extremely improved, too. This newly developed control strategy combines the advantage of GA-s and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed GA based HF (GAHF) controller is tested on a threearea deregulated power system under different operating conditions and contract variations. The results of the proposed GAHF controller are compared with those of Multi Stage Fuzzy (MSF) controller, robust mixed H2/H∞ and classical PID controllers through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.

Keywords: AGC, Hybrid Fuzzy Controller, Deregulated Power System, Power System Control, GAs.

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Authors: M. G. Papoutsidakis, G. Chamilothoris, F. Dailami, N. Larsen, A Pipe

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Due to their high power-to-weight ratio and low cost, pneumatic actuators are attractive for robotics and automation applications; however, achieving fast and accurate control of their position have been known as a complex control problem. A methodology for obtaining high position accuracy with a linear pneumatic actuator is presented. During experimentation with a number of PID classical control approaches over many operations of the pneumatic system, the need for frequent manual re-tuning of the controller could not be eliminated. The reason for this problem is thermal and energy losses inside the cylinder body due to the complex friction forces developed by the piston displacements. Although PD controllers performed very well over short periods, it was necessary in our research project to introduce some form of automatic gain-scheduling to achieve good long-term performance. We chose a fuzzy logic system to do this, which proved to be an easily designed and robust approach. Since the PD approach showed very good behaviour in terms of position accuracy and settling time, it was incorporated into a modified form of the 1st order Tagaki- Sugeno fuzzy method to build an overall controller. This fuzzy gainscheduler uses an input variable which automatically changes the PD gain values of the controller according to the frequency of repeated system operations. Performance of the new controller was significantly improved and the need for manual re-tuning was eliminated without a decrease in performance. The performance of the controller operating with the above method is going to be tested through a high-speed web network (GRID) for research purposes.

Keywords: Fuzzy logic, gain scheduling, leaky integrator, pneumatic actuator.

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